In this article, we examine two problems
[1]}, [2]}
designed to highlight the shortcomings of
connectionist models that rely on
generalisation based on featural similarity or overlap
and describe solutions to these long-standingIn the eyes of its creator, the identity learning task has remained an unresolved problem for neural network models for over two decades [1]}, [4]}. challenges.
The identity and rule learning tasks themselves are described
in more detail in Sections  and ,
but the basic idea in each case is fairly simple.
Both tasks require generalising to test data that contains
elements - digits or words - that were not seen during training.
